Book Image

Learning PySpark

By : Tomasz Drabas, Denny Lee
Book Image

Learning PySpark

By: Tomasz Drabas, Denny Lee

Overview of this book

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark. You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.
Table of Contents (20 chapters)
Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Databricks Jobs


If you are using the Databricks product, an easy way to go from development from your Databricks notebooks to production is to use the Databricks Jobs feature. It will allow you to:

  • Schedule your Databricks notebook to run on an existing or new cluster

  • Schedule at your desired frequency (from minutes to months)

  • Schedule time out and retries for your job

  • Be alerted when the job starts, completes, and/or errors out

  • View historical job runs as well as review the history of the individual notebook job runs

This capability greatly simplifies the scheduling and production workflow of your job submissions. Note that you will need to upgrade your Databricks subscription (from Community edition) to use this feature.

To use this feature, go to the Databricks Jobs menu and click on Create Job. From here, fill out the job name and then choose the notebook that you want to turn into a job, as shown in the following screenshot:

Once you have chosen your notebook, you can also choose whether to...